Domain knowledge integration into deep learning for typhoon intensity classification
Abstract In this report, we propose a deep learning technique for high-accuracy estimation of the intensity class of a typhoon from a single satellite image, by incorporating meteorological domain knowledge. By using the Visual Geometric Group’s model, VGG-16, with images preprocessed with fisheye d...
Guardado en:
Autores principales: | Maiki Higa, Shinya Tanahara, Yoshitaka Adachi, Natsumi Ishiki, Shin Nakama, Hiroyuki Yamada, Kosuke Ito, Asanobu Kitamoto, Ryota Miyata |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Nature Portfolio
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/4ce72493e0a74592836482ed6f7f41f9 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Integrating nonstationary behaviors of typhoon and non-typhoon extreme rainfall events in East Asia
por: Chanyoung Son, et al.
Publicado: (2017) -
Applicability of a nationwide flood forecasting system for Typhoon Hagibis 2019
por: Wenchao Ma, et al.
Publicado: (2021) -
A new approach for location-specific seasonal outlooks of typhoon and super typhoon frequency across the Western North Pacific region
por: Andrew D. Magee, et al.
Publicado: (2021) -
Potential of rice landraces with strong culms as genetic resources for improving lodging resistance against super typhoons
por: Tomohiro Nomura, et al.
Publicado: (2021) -
Typhoon storm surge in the southeast Chinese mainland modulated by ENSO
por: Xingru Feng, et al.
Publicado: (2021)